Overview

Dataset statistics

Number of variables19
Number of observations200
Missing cells107
Missing cells (%)2.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.4 KiB
Average record size in memory155.7 B

Variable types

Numeric3
Text6
Categorical10

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-22253/F/1/datasetView.do

Alerts

순번 is highly overall correlated with 시군구명 and 3 other fieldsHigh correlation
번 is highly overall correlated with 시군구명 and 1 other fieldsHigh correlation
지 is highly overall correlated with 기본계획명High correlation
시군구명 is highly overall correlated with 순번 and 5 other fieldsHigh correlation
지목 is highly overall correlated with 건폐율High correlation
지구유형 is highly overall correlated with 시군구명 and 2 other fieldsHigh correlation
사업시행방식 is highly overall correlated with 번 and 3 other fieldsHigh correlation
시행자 구분 is highly overall correlated with 사업시행방식 and 2 other fieldsHigh correlation
시행단계 is highly overall correlated with 시행자 구분 and 1 other fieldsHigh correlation
정비유형 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
생활권유형 is highly overall correlated with 순번 and 3 other fieldsHigh correlation
건폐율 is highly overall correlated with 지목 and 4 other fieldsHigh correlation
기본계획명 is highly overall correlated with 순번 and 6 other fieldsHigh correlation
지목 is highly imbalanced (85.7%)Imbalance
지구유형 is highly imbalanced (61.2%)Imbalance
생활권유형 is highly imbalanced (61.7%)Imbalance
기본계획명 is highly imbalanced (56.4%)Imbalance
용적률 has 35 (17.5%) missing valuesMissing
고시번호 has 36 (18.0%) missing valuesMissing
고시일 has 36 (18.0%) missing valuesMissing
순번 has unique valuesUnique
정비 구역명 has unique valuesUnique
정비구역 면적(제곱미터) has unique valuesUnique
지 has 69 (34.5%) zerosZeros

Reproduction

Analysis started2024-01-06 09:22:32.704480
Analysis finished2024-01-06 09:22:41.438752
Duration8.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.5
Minimum1
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-01-06T09:22:41.578200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.95
Q150.75
median100.5
Q3150.25
95-th percentile190.05
Maximum200
Range199
Interquartile range (IQR)99.5

Descriptive statistics

Standard deviation57.879185
Coefficient of variation (CV)0.57591228
Kurtosis-1.2
Mean100.5
Median Absolute Deviation (MAD)50
Skewness0
Sum20100
Variance3350
MonotonicityStrictly increasing
2024-01-06T09:22:41.912311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
139 1
 
0.5%
129 1
 
0.5%
130 1
 
0.5%
131 1
 
0.5%
132 1
 
0.5%
133 1
 
0.5%
134 1
 
0.5%
135 1
 
0.5%
136 1
 
0.5%
Other values (190) 190
95.0%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
200 1
0.5%
199 1
0.5%
198 1
0.5%
197 1
0.5%
196 1
0.5%
195 1
0.5%
194 1
0.5%
193 1
0.5%
192 1
0.5%
191 1
0.5%

정비 구역명
Text

UNIQUE 

Distinct200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-01-06T09:22:42.497185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length20
Mean length11.225
Min length3

Characters and Unicode

Total characters2245
Distinct characters164
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)100.0%

Sample

1st row미아제11구역 주택재개발정비사업
2nd row미아9-2주택재건축정비구역
3rd row미아4-1구역주택재건축정비사업
4th row미아2재정비촉진구역 주택재개발
5th row미아3재정비촉진구역 주택재개발정비사업
ValueCountFrequency (%)
주택재개발정비사업 13
 
4.2%
재정비촉진구역 7
 
2.3%
도시환경정비사업 6
 
1.9%
주거환경개선사업 6
 
1.9%
수송 5
 
1.6%
성곽마을 5
 
1.6%
주택재건축정비사업 5
 
1.6%
도시정비형 4
 
1.3%
성수전략정비구역 4
 
1.3%
재개발정비사업 3
 
1.0%
Other values (234) 250
81.2%
2024-01-06T09:22:43.498716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 119
 
5.3%
108
 
4.8%
정 107
 
4.8%
1 102
 
4.5%
비 102
 
4.5%
역 94
 
4.2%
구 93
 
4.1%
재 86
 
3.8%
업 58
 
2.6%
사 57
 
2.5%
Other values (154) 1319
58.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1667
74.3%
Decimal Number 337
 
15.0%
Dash Punctuation 119
 
5.3%
Space Separator 108
 
4.8%
Close Punctuation 6
 
0.3%
Open Punctuation 6
 
0.3%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 107
 
6.4%
비 102
 
6.1%
역 94
 
5.6%
구 93
 
5.6%
재 86
 
5.2%
업 58
 
3.5%
사 57
 
3.4%
주 49
 
2.9%
개 44
 
2.6%
진 41
 
2.5%
Other values (139) 936
56.1%
Decimal Number
ValueCountFrequency (%)
1 102
30.3%
2 52
15.4%
3 44
13.1%
0 42
12.5%
4 28
 
8.3%
5 23
 
6.8%
8 15
 
4.5%
6 11
 
3.3%
7 10
 
3.0%
9 10
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 119
100.0%
Space Separator
ValueCountFrequency (%)
108
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Other Punctuation
ValueCountFrequency (%)
· 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1667
74.3%
Common 578
 
25.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 107
 
6.4%
비 102
 
6.1%
역 94
 
5.6%
구 93
 
5.6%
재 86
 
5.2%
업 58
 
3.5%
사 57
 
3.4%
주 49
 
2.9%
개 44
 
2.6%
진 41
 
2.5%
Other values (139) 936
56.1%
Common
ValueCountFrequency (%)
- 119
20.6%
108
18.7%
1 102
17.6%
2 52
9.0%
3 44
 
7.6%
0 42
 
7.3%
4 28
 
4.8%
5 23
 
4.0%
8 15
 
2.6%
6 11
 
1.9%
Other values (5) 34
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1667
74.3%
ASCII 576
 
25.7%
None 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 119
20.7%
108
18.8%
1 102
17.7%
2 52
9.0%
3 44
 
7.6%
0 42
 
7.3%
4 28
 
4.9%
5 23
 
4.0%
8 15
 
2.6%
6 11
 
1.9%
Other values (4) 32
 
5.6%
Hangul
ValueCountFrequency (%)
정 107
 
6.4%
비 102
 
6.1%
역 94
 
5.6%
구 93
 
5.6%
재 86
 
5.2%
업 58
 
3.5%
사 57
 
3.4%
주 49
 
2.9%
개 44
 
2.6%
진 41
 
2.5%
Other values (139) 936
56.1%
None
ValueCountFrequency (%)
· 2
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
중구
52 
종로구
35 
서대문구
19 
동대문구
16 
영등포구
13 
Other values (14)
65 

Length

Max length4
Median length3.5
Mean length2.98
Min length2

Unique

Unique3 ?
Unique (%)1.5%

Sample

1st row강북구
2nd row강북구
3rd row강북구
4th row강북구
5th row강북구

Common Values

ValueCountFrequency (%)
중구 52
26.0%
종로구 35
17.5%
서대문구 19
 
9.5%
동대문구 16
 
8.0%
영등포구 13
 
6.5%
성북구 11
 
5.5%
용산구 11
 
5.5%
성동구 7
 
3.5%
동작구 7
 
3.5%
관악구 7
 
3.5%
Other values (9) 22
11.0%

Length

2024-01-06T09:22:43.949944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중구 52
26.0%
종로구 35
17.5%
서대문구 19
 
9.5%
동대문구 16
 
8.0%
영등포구 13
 
6.5%
성북구 11
 
5.5%
용산구 11
 
5.5%
강북구 7
 
3.5%
관악구 7
 
3.5%
동작구 7
 
3.5%
Other values (9) 22
11.0%

법정동명
Text

Distinct101
Distinct (%)50.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-01-06T09:22:44.533539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.705
Min length2

Characters and Unicode

Total characters741
Distinct characters112
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)28.0%

Sample

1st row미아동
2nd row미아동
3rd row미아동
4th row미아동
5th row미아동
ValueCountFrequency (%)
서소문동 12
 
6.0%
미아동 7
 
3.5%
용두동 6
 
3.0%
을지로3가 6
 
3.0%
창천동 5
 
2.5%
수송동 5
 
2.5%
수표동 4
 
2.0%
봉천동 4
 
2.0%
청진동 4
 
2.0%
남대문로5가 4
 
2.0%
Other values (91) 143
71.5%
2024-01-06T09:22:45.411722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 167
22.5%
가 57
 
7.7%
로 34
 
4.6%
문 26
 
3.5%
신 18
 
2.4%
수 16
 
2.2%
서 15
 
2.0%
소 15
 
2.0%
3 15
 
2.0%
1 14
 
1.9%
Other values (102) 364
49.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 686
92.6%
Decimal Number 55
 
7.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 167
24.3%
가 57
 
8.3%
로 34
 
5.0%
문 26
 
3.8%
신 18
 
2.6%
수 16
 
2.3%
서 15
 
2.2%
소 15
 
2.2%
천 12
 
1.7%
정 10
 
1.5%
Other values (95) 316
46.1%
Decimal Number
ValueCountFrequency (%)
3 15
27.3%
1 14
25.5%
2 12
21.8%
5 8
14.5%
4 4
 
7.3%
6 1
 
1.8%
7 1
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 686
92.6%
Common 55
 
7.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 167
24.3%
가 57
 
8.3%
로 34
 
5.0%
문 26
 
3.8%
신 18
 
2.6%
수 16
 
2.3%
서 15
 
2.2%
소 15
 
2.2%
천 12
 
1.7%
정 10
 
1.5%
Other values (95) 316
46.1%
Common
ValueCountFrequency (%)
3 15
27.3%
1 14
25.5%
2 12
21.8%
5 8
14.5%
4 4
 
7.3%
6 1
 
1.8%
7 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 686
92.6%
ASCII 55
 
7.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 167
24.3%
가 57
 
8.3%
로 34
 
5.0%
문 26
 
3.8%
신 18
 
2.6%
수 16
 
2.3%
서 15
 
2.2%
소 15
 
2.2%
천 12
 
1.7%
정 10
 
1.5%
Other values (95) 316
46.1%
ASCII
ValueCountFrequency (%)
3 15
27.3%
1 14
25.5%
2 12
21.8%
5 8
14.5%
4 4
 
7.3%
6 1
 
1.8%
7 1
 
1.8%

번
Real number (ℝ)

HIGH CORRELATION 

Distinct147
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean222.315
Minimum1
Maximum3590
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-01-06T09:22:45.895391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q143.75
median118.5
Q3266.25
95-th percentile784.35
Maximum3590
Range3589
Interquartile range (IQR)222.5

Descriptive statistics

Standard deviation351.28569
Coefficient of variation (CV)1.5801259
Kurtosis43.542059
Mean222.315
Median Absolute Deviation (MAD)89
Skewness5.3587977
Sum44463
Variance123401.63
MonotonicityNot monotonic
2024-01-06T09:22:46.395089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58 5
 
2.5%
30 5
 
2.5%
120 5
 
2.5%
40 4
 
2.0%
1 4
 
2.0%
60 4
 
2.0%
6 3
 
1.5%
18 3
 
1.5%
146 3
 
1.5%
95 3
 
1.5%
Other values (137) 161
80.5%
ValueCountFrequency (%)
1 4
2.0%
2 1
 
0.5%
3 1
 
0.5%
4 1
 
0.5%
5 3
1.5%
6 3
1.5%
7 1
 
0.5%
8 1
 
0.5%
9 2
1.0%
11 2
1.0%
ValueCountFrequency (%)
3590 1
0.5%
1656 1
0.5%
1200 1
0.5%
1190 1
0.5%
1182 1
0.5%
922 1
0.5%
891 1
0.5%
883 1
0.5%
808 1
0.5%
791 1
0.5%

지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.6
Minimum0
Maximum1267
Zeros69
Zeros (%)34.5%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-01-06T09:22:46.847004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q312
95-th percentile167.2
Maximum1267
Range1267
Interquartile range (IQR)12

Descriptive statistics

Standard deviation167.78761
Coefficient of variation (CV)3.6005926
Kurtosis27.871027
Mean46.6
Median Absolute Deviation (MAD)1
Skewness5.1149497
Sum9320
Variance28152.683
MonotonicityNot monotonic
2024-01-06T09:22:47.318069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 69
34.5%
1 35
17.5%
3 8
 
4.0%
2 7
 
3.5%
5 6
 
3.0%
6 5
 
2.5%
4 5
 
2.5%
12 4
 
2.0%
14 4
 
2.0%
10 4
 
2.0%
Other values (44) 53
26.5%
ValueCountFrequency (%)
0 69
34.5%
1 35
17.5%
2 7
 
3.5%
3 8
 
4.0%
4 5
 
2.5%
5 6
 
3.0%
6 5
 
2.5%
7 3
 
1.5%
8 3
 
1.5%
9 2
 
1.0%
ValueCountFrequency (%)
1267 1
0.5%
1061 1
0.5%
1008 1
0.5%
712 1
0.5%
678 1
0.5%
641 1
0.5%
552 1
0.5%
495 1
0.5%
373 1
0.5%
266 1
0.5%

지목
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
대
190 
도로
 
4
하천
 
2
종교용지
 
1
잡종지
 
1
Other values (2)
 
2

Length

Max length4
Median length1
Mean length1.075
Min length1

Unique

Unique4 ?
Unique (%)2.0%

Sample

1st row대
2nd row대
3rd row대
4th row대
5th row대

Common Values

ValueCountFrequency (%)
대 190
95.0%
도로 4
 
2.0%
하천 2
 
1.0%
종교용지 1
 
0.5%
잡종지 1
 
0.5%
임야 1
 
0.5%
공장용지 1
 
0.5%

Length

2024-01-06T09:22:47.860151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-06T09:22:48.217944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대 190
95.0%
도로 4
 
2.0%
하천 2
 
1.0%
종교용지 1
 
0.5%
잡종지 1
 
0.5%
임야 1
 
0.5%
공장용지 1
 
0.5%

지구유형
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
정비(예정)구역
160 
재정비촉진지구
28 
정비구역이 아닌 구역
 
6
재정비촉진+뉴타운지구
 
3
균형발전촉진지구
 
2

Length

Max length11
Median length8
Mean length7.98
Min length5

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row정비(예정)구역
2nd row정비(예정)구역
3rd row정비(예정)구역
4th row정비(예정)구역
5th row정비(예정)구역

Common Values

ValueCountFrequency (%)
정비(예정)구역 160
80.0%
재정비촉진지구 28
 
14.0%
정비구역이 아닌 구역 6
 
3.0%
재정비촉진+뉴타운지구 3
 
1.5%
균형발전촉진지구 2
 
1.0%
뉴타운지구 1
 
0.5%

Length

2024-01-06T09:22:48.623747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-06T09:22:48.979546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정비(예정)구역 160
75.5%
재정비촉진지구 28
 
13.2%
정비구역이 6
 
2.8%
아닌 6
 
2.8%
구역 6
 
2.8%
재정비촉진+뉴타운지구 3
 
1.4%
균형발전촉진지구 2
 
0.9%
뉴타운지구 1
 
0.5%

사업시행방식
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
도시정비형재개발
99 
(촉)재개발
22 
주택정비형재개발
15 
재건축(단독)
11 
(촉)도시환경정비
11 
Other values (10)
42 

Length

Max length9
Median length8
Mean length7.59
Min length3

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row주택정비형재개발
2nd row재건축(단독)
3rd row재건축(단독)
4th row(촉)재개발
5th row(촉)재개발

Common Values

ValueCountFrequency (%)
도시정비형재개발 99
49.5%
(촉)재개발 22
 
11.0%
주택정비형재개발 15
 
7.5%
재건축(단독) 11
 
5.5%
(촉)도시환경정비 11
 
5.5%
재개발(주택) 10
 
5.0%
도시정비형 재개발 6
 
3.0%
주거환경개선 6
 
3.0%
재건축(공동) 5
 
2.5%
주택정비형 재개발 5
 
2.5%
Other values (5) 10
 
5.0%

Length

2024-01-06T09:22:49.390755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
도시정비형재개발 99
46.9%
촉)재개발 22
 
10.4%
주택정비형재개발 15
 
7.1%
재건축(단독 11
 
5.2%
촉)도시환경정비 11
 
5.2%
재개발 11
 
5.2%
재개발(주택 10
 
4.7%
도시정비형 6
 
2.8%
주거환경개선 6
 
2.8%
재건축(공동 5
 
2.4%
Other values (6) 15
 
7.1%

시행자 구분
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
조합(단독)
93 
토지등소유자
92 
시장군수
 
7
주택공사등
 
5
공동(조합조합이외의자)
 
3

Length

Max length12
Median length6
Mean length5.995
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row조합(단독)
2nd row조합(단독)
3rd row조합(단독)
4th row조합(단독)
5th row조합(단독)

Common Values

ValueCountFrequency (%)
조합(단독) 93
46.5%
토지등소유자 92
46.0%
시장군수 7
 
3.5%
주택공사등 5
 
2.5%
공동(조합조합이외의자) 3
 
1.5%

Length

2024-01-06T09:22:49.846620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-06T09:22:50.213699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
조합(단독 93
46.5%
토지등소유자 92
46.0%
시장군수 7
 
3.5%
주택공사등 5
 
2.5%
공동(조합조합이외의자 3
 
1.5%

시행단계
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
정비구역지정
105 
조합설립인가
59 
추진위구성
25 
사업시행인가
 
5
사업시행계획인가
 
2
Other values (3)
 
4

Length

Max length12
Median length6
Mean length5.955
Min length5

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row추진위구성
2nd row조합설립인가
3rd row조합설립인가
4th row조합설립인가
5th row조합설립인가

Common Values

ValueCountFrequency (%)
정비구역지정 105
52.5%
조합설립인가 59
29.5%
추진위구성 25
 
12.5%
사업시행인가 5
 
2.5%
사업시행계획인가 2
 
1.0%
추진위원회승인 2
 
1.0%
사업시행자(LH) 지정 1
 
0.5%
주민대표회의구성통지 1
 
0.5%

Length

2024-01-06T09:22:50.613162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-06T09:22:50.982468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정비구역지정 105
52.2%
조합설립인가 59
29.4%
추진위구성 25
 
12.4%
사업시행인가 5
 
2.5%
사업시행계획인가 2
 
1.0%
추진위원회승인 2
 
1.0%
사업시행자(lh 1
 
0.5%
지정 1
 
0.5%
주민대표회의구성통지 1
 
0.5%

정비유형
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
<NA>
129 
전면
59 
수복(전면)
 
9
수복
 
3

Length

Max length6
Median length4
Mean length3.47
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전면
2nd row<NA>
3rd row<NA>
4th row수복(전면)
5th row수복(전면)

Common Values

ValueCountFrequency (%)
<NA> 129
64.5%
전면 59
29.5%
수복(전면) 9
 
4.5%
수복 3
 
1.5%

Length

2024-01-06T09:22:51.631301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-06T09:22:51.987888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 129
64.5%
전면 59
29.5%
수복(전면 9
 
4.5%
수복 3
 
1.5%

생활권유형
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
<NA>
169 
D(뉴타운)
 
15
A(소생활권)
 
8
C(대생활권)
 
6
B(중생활권)
 
2

Length

Max length7
Median length4
Mean length4.39
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC(대생활권)
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 169
84.5%
D(뉴타운) 15
 
7.5%
A(소생활권) 8
 
4.0%
C(대생활권) 6
 
3.0%
B(중생활권) 2
 
1.0%

Length

2024-01-06T09:22:52.410526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-06T09:22:52.870173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 169
84.5%
d(뉴타운 15
 
7.5%
a(소생활권 8
 
4.0%
c(대생활권 6
 
3.0%
b(중생활권 2
 
1.0%

정비구역 면적(제곱미터)
Text

UNIQUE 

Distinct200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-01-06T09:22:53.630523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.54
Min length3

Characters and Unicode

Total characters1108
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)100.0%

Sample

1st row35,891
2nd row102,847
3rd row51,265
4th row179,566
5th row57,553
ValueCountFrequency (%)
35,891 1
 
0.5%
1,649 1
 
0.5%
1,646 1
 
0.5%
3,698 1
 
0.5%
3,017 1
 
0.5%
2,850 1
 
0.5%
628 1
 
0.5%
803 1
 
0.5%
662 1
 
0.5%
4,269 1
 
0.5%
Other values (190) 190
95.0%
2024-01-06T09:22:54.948220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 183
16.5%
1 141
12.7%
2 106
9.6%
0 99
8.9%
3 97
8.8%
6 88
7.9%
8 85
7.7%
7 82
7.4%
5 80
7.2%
4 74
6.7%
Other values (2) 73
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 920
83.0%
Other Punctuation 188
 
17.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 141
15.3%
2 106
11.5%
0 99
10.8%
3 97
10.5%
6 88
9.6%
8 85
9.2%
7 82
8.9%
5 80
8.7%
4 74
8.0%
9 68
7.4%
Other Punctuation
ValueCountFrequency (%)
, 183
97.3%
. 5
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1108
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 183
16.5%
1 141
12.7%
2 106
9.6%
0 99
8.9%
3 97
8.8%
6 88
7.9%
8 85
7.7%
7 82
7.4%
5 80
7.2%
4 74
6.7%
Other values (2) 73
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 183
16.5%
1 141
12.7%
2 106
9.6%
0 99
8.9%
3 97
8.8%
6 88
7.9%
8 85
7.7%
7 82
7.4%
5 80
7.2%
4 74
6.7%
Other values (2) 73
 
6.6%

용적률
Text

MISSING 

Distinct93
Distinct (%)56.4%
Missing35
Missing (%)17.5%
Memory size1.7 KiB
2024-01-06T09:22:55.546557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length3
Mean length6.5818182
Min length2

Characters and Unicode

Total characters1086
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)42.4%

Sample

1st row190
2nd row231
3rd row197
4th row230
5th row229
ValueCountFrequency (%)
기준)600(허용)700(상한)800이하 12
 
7.3%
800 12
 
7.3%
기준)600(허용)800이하 11
 
6.7%
250 7
 
4.2%
600 6
 
3.6%
800이하 5
 
3.0%
기준)600(허용)800 4
 
2.4%
230 4
 
2.4%
400 4
 
2.4%
317 3
 
1.8%
Other values (83) 97
58.8%
2024-01-06T09:22:56.586735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 266
24.5%
( 87
 
8.0%
) 87
 
8.0%
6 65
 
6.0%
8 62
 
5.7%
2 60
 
5.5%
이 50
 
4.6%
하 50
 
4.6%
5 36
 
3.3%
7 36
 
3.3%
Other values (10) 287
26.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 638
58.7%
Other Letter 274
25.2%
Open Punctuation 87
 
8.0%
Close Punctuation 87
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 266
41.7%
6 65
 
10.2%
8 62
 
9.7%
2 60
 
9.4%
5 36
 
5.6%
7 36
 
5.6%
3 32
 
5.0%
9 29
 
4.5%
4 28
 
4.4%
1 24
 
3.8%
Other Letter
ValueCountFrequency (%)
이 50
18.2%
하 50
18.2%
준 34
12.4%
허 34
12.4%
용 34
12.4%
기 34
12.4%
상 19
 
6.9%
한 19
 
6.9%
Open Punctuation
ValueCountFrequency (%)
( 87
100.0%
Close Punctuation
ValueCountFrequency (%)
) 87
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 812
74.8%
Hangul 274
 
25.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 266
32.8%
( 87
 
10.7%
) 87
 
10.7%
6 65
 
8.0%
8 62
 
7.6%
2 60
 
7.4%
5 36
 
4.4%
7 36
 
4.4%
3 32
 
3.9%
9 29
 
3.6%
Other values (2) 52
 
6.4%
Hangul
ValueCountFrequency (%)
이 50
18.2%
하 50
18.2%
준 34
12.4%
허 34
12.4%
용 34
12.4%
기 34
12.4%
상 19
 
6.9%
한 19
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 812
74.8%
Hangul 274
 
25.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 266
32.8%
( 87
 
10.7%
) 87
 
10.7%
6 65
 
8.0%
8 62
 
7.6%
2 60
 
7.4%
5 36
 
4.4%
7 36
 
4.4%
3 32
 
3.9%
9 29
 
3.6%
Other values (2) 52
 
6.4%
Hangul
ValueCountFrequency (%)
이 50
18.2%
하 50
18.2%
준 34
12.4%
허 34
12.4%
용 34
12.4%
기 34
12.4%
상 19
 
6.9%
한 19
 
6.9%

건폐율
Categorical

HIGH CORRELATION 

Distinct39
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
60이하
55 
<NA>
46 
60
20 
50이하
8 
21
7 
Other values (34)
64 

Length

Max length6
Median length4
Mean length3.145
Min length2

Unique

Unique22 ?
Unique (%)11.0%

Sample

1st row60
2nd row20
3rd row21
4th row16
5th row25

Common Values

ValueCountFrequency (%)
60이하 55
27.5%
<NA> 46
23.0%
60 20
 
10.0%
50이하 8
 
4.0%
21 7
 
3.5%
25 7
 
3.5%
20 5
 
2.5%
30 4
 
2.0%
23 4
 
2.0%
35 3
 
1.5%
Other values (29) 41
20.5%

Length

2024-01-06T09:22:56.852770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
60이하 55
27.5%
na 46
23.0%
60 20
 
10.0%
50이하 8
 
4.0%
21 7
 
3.5%
25 7
 
3.5%
20 5
 
2.5%
30 4
 
2.0%
23 4
 
2.0%
59 3
 
1.5%
Other values (29) 41
20.5%

기본계획명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct17
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
<NA>
142 
도시주거환경정비
 
14
2025 서울시 도시 및 주거환경정비기본계획
 
13
도시주거환경정비 기본계획
 
11
영등포뉴타운지구 개발기본계획
 
4
Other values (12)
16 

Length

Max length43
Median length4
Mean length7.545
Min length4

Unique

Unique10 ?
Unique (%)5.0%

Sample

1st row도시주거환경정비
2nd row<NA>
3rd row<NA>
4th row도시주거환경정비
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 142
71.0%
도시주거환경정비 14
 
7.0%
2025 서울시 도시 및 주거환경정비기본계획 13
 
6.5%
도시주거환경정비 기본계획 11
 
5.5%
영등포뉴타운지구 개발기본계획 4
 
2.0%
성수전략정비구역 제1종지구단위계획 4
 
2.0%
도시 및 주거환경정비 기본계획 2
 
1.0%
용산 제1종지구단위계획 1
 
0.5%
신영동 너와나우리마을 주거환경관리사업 1
 
0.5%
종로구 부암동 성곽마을(창의문 백악,인왕마을) 관리형 주거환경개선사업 정비구역 1
 
0.5%
Other values (7) 7
 
3.5%

Length

2024-01-06T09:22:57.119873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 142
48.3%
도시주거환경정비 25
 
8.5%
도시 15
 
5.1%
및 15
 
5.1%
2025 13
 
4.4%
서울시 13
 
4.4%
주거환경정비기본계획 13
 
4.4%
기본계획 13
 
4.4%
제1종지구단위계획 5
 
1.7%
영등포뉴타운지구 4
 
1.4%
Other values (27) 36
 
12.2%

고시번호
Text

MISSING 

Distinct104
Distinct (%)63.4%
Missing36
Missing (%)18.0%
Memory size1.7 KiB
2024-01-06T09:22:57.569143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length10
Mean length12.445122
Min length6

Characters and Unicode

Total characters2041
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)51.2%

Sample

1st row제2010-490호
2nd row제2009-301호
3rd row제2009-307호
4th row제2020-423호
5th row제2020-423호
ValueCountFrequency (%)
서울특별시고시 29
 
11.6%
고시 19
 
7.6%
서울특별시 16
 
6.4%
제2016-310호 12
 
4.8%
제2020-233호 12
 
4.8%
제2010-485호 5
 
2.0%
제2021-357호 5
 
2.0%
제2017-311호 5
 
2.0%
제2013-118호 5
 
2.0%
서울시 5
 
2.0%
Other values (105) 137
54.8%
2024-01-06T09:22:58.614099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 269
13.2%
0 263
12.9%
1 205
10.0%
호 156
 
7.6%
제 155
 
7.6%
- 131
 
6.4%
시 107
 
5.2%
3 102
 
5.0%
86
 
4.2%
9 63
 
3.1%
Other values (22) 504
24.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1111
54.4%
Other Letter 713
34.9%
Dash Punctuation 131
 
6.4%
Space Separator 86
 
4.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 156
21.9%
제 155
21.7%
시 107
15.0%
고 57
 
8.0%
서 51
 
7.2%
울 50
 
7.0%
특 45
 
6.3%
별 45
 
6.3%
구 9
 
1.3%
문 8
 
1.1%
Other values (10) 30
 
4.2%
Decimal Number
ValueCountFrequency (%)
2 269
24.2%
0 263
23.7%
1 205
18.5%
3 102
 
9.2%
9 63
 
5.7%
7 45
 
4.1%
8 43
 
3.9%
5 43
 
3.9%
4 39
 
3.5%
6 39
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 131
100.0%
Space Separator
ValueCountFrequency (%)
86
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1328
65.1%
Hangul 713
34.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 156
21.9%
제 155
21.7%
시 107
15.0%
고 57
 
8.0%
서 51
 
7.2%
울 50
 
7.0%
특 45
 
6.3%
별 45
 
6.3%
구 9
 
1.3%
문 8
 
1.1%
Other values (10) 30
 
4.2%
Common
ValueCountFrequency (%)
2 269
20.3%
0 263
19.8%
1 205
15.4%
- 131
9.9%
3 102
 
7.7%
86
 
6.5%
9 63
 
4.7%
7 45
 
3.4%
8 43
 
3.2%
5 43
 
3.2%
Other values (2) 78
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1328
65.1%
Hangul 713
34.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 269
20.3%
0 263
19.8%
1 205
15.4%
- 131
9.9%
3 102
 
7.7%
86
 
6.5%
9 63
 
4.7%
7 45
 
3.4%
8 43
 
3.2%
5 43
 
3.2%
Other values (2) 78
 
5.9%
Hangul
ValueCountFrequency (%)
호 156
21.9%
제 155
21.7%
시 107
15.0%
고 57
 
8.0%
서 51
 
7.2%
울 50
 
7.0%
특 45
 
6.3%
별 45
 
6.3%
구 9
 
1.3%
문 8
 
1.1%
Other values (10) 30
 
4.2%

고시일
Text

MISSING 

Distinct97
Distinct (%)59.1%
Missing36
Missing (%)18.0%
Memory size1.7 KiB
2024-01-06T09:22:59.220237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9939024
Min length9

Characters and Unicode

Total characters1639
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)43.9%

Sample

1st row2010-12-30
2nd row2009-07-30
3rd row2009-08-06
4th row2020-10-22
5th row2020-10-22
ValueCountFrequency (%)
2020-06-04 12
 
7.3%
2016-10-06 12
 
7.3%
2010-12-30 6
 
3.7%
2017-08-24 5
 
3.0%
2013-04-18 5
 
3.0%
2021-07-15 5
 
3.0%
2011-02-17 4
 
2.4%
2017-05-04 4
 
2.4%
2021-02-04 4
 
2.4%
2013-07-11 3
 
1.8%
Other values (87) 104
63.4%
2024-01-06T09:23:00.630617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 443
27.0%
- 327
20.0%
2 291
17.8%
1 237
14.5%
6 66
 
4.0%
4 58
 
3.5%
7 50
 
3.1%
8 49
 
3.0%
3 47
 
2.9%
9 40
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1312
80.0%
Dash Punctuation 327
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 443
33.8%
2 291
22.2%
1 237
18.1%
6 66
 
5.0%
4 58
 
4.4%
7 50
 
3.8%
8 49
 
3.7%
3 47
 
3.6%
9 40
 
3.0%
5 31
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 327
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1639
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 443
27.0%
- 327
20.0%
2 291
17.8%
1 237
14.5%
6 66
 
4.0%
4 58
 
3.5%
7 50
 
3.1%
8 49
 
3.0%
3 47
 
2.9%
9 40
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1639
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 443
27.0%
- 327
20.0%
2 291
17.8%
1 237
14.5%
6 66
 
4.0%
4 58
 
3.5%
7 50
 
3.1%
8 49
 
3.0%
3 47
 
2.9%
9 40
 
2.4%

Interactions

2024-01-06T09:22:39.388183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T09:22:37.806053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T09:22:38.599363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T09:22:39.651033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T09:22:38.073514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T09:22:38.869740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T09:22:39.911717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T09:22:38.344307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T09:22:39.130630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-06T09:23:00.972029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번시군구명번지지목지구유형사업시행방식시행자 구분시행단계정비유형생활권유형용적률건폐율기본계획명고시일
순번1.0000.9530.3840.2130.0700.4700.7730.8090.5780.6930.7420.9570.7700.9060.988
시군구명0.9531.0000.7990.4350.2630.8420.8550.6970.6860.8310.9290.9630.8810.9090.995
번0.3840.7991.0000.0640.0000.8290.8440.3630.3360.0000.0000.9310.7760.0000.846
지0.2130.4350.0641.0000.7000.0000.2910.5030.0000.0000.0000.9640.6310.9370.934
지목0.0700.2630.0000.7001.0000.0000.0000.2890.2300.3700.0620.9010.8940.5900.572
지구유형0.4700.8420.8290.0000.0001.0000.7370.2480.4830.0000.6080.8970.7130.9340.996
사업시행방식0.7730.8550.8440.2910.0000.7371.0000.8930.7340.6610.6520.9710.9200.8640.984
시행자 구분0.8090.6970.3630.5030.2890.2480.8931.0000.7430.1320.5740.9250.8380.7890.983
시행단계0.5780.6860.3360.0000.2300.4830.7340.7431.0000.2930.3870.8450.8860.5100.944
정비유형0.6930.8310.0000.0000.3700.0000.6610.1320.2931.0000.3510.9830.5780.5780.991
생활권유형0.7420.9290.0000.0000.0620.6080.6520.5740.3870.3511.0000.9670.5530.8120.996
용적률0.9570.9630.9310.9640.9010.8970.9710.9250.8450.9830.9671.0000.9930.9980.997
건폐율0.7700.8810.7760.6310.8940.7130.9200.8380.8860.5780.5530.9931.0000.9360.994
기본계획명0.9060.9090.0000.9370.5900.9340.8640.7890.5100.5780.8120.9980.9361.0000.994
고시일0.9880.9950.8460.9340.5720.9960.9840.9830.9440.9910.9960.9970.9940.9941.000
2024-01-06T09:23:01.487791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건폐율시군구명시행단계지구유형시행자 구분정비유형사업시행방식생활권유형지목기본계획명
건폐율1.0000.3630.5480.3570.5110.3030.5280.2320.5760.557
시군구명0.3631.0000.3610.5720.4200.6480.4760.5580.1140.589
시행단계0.5480.3611.0000.2910.5750.2260.4180.3120.1240.228
지구유형0.3570.5720.2911.0000.1690.0000.4450.6110.0000.752
시행자 구분0.5110.4200.5750.1691.0000.0940.5850.2480.1880.489
정비유형0.3030.6480.2260.0000.0941.0000.4830.3280.3560.407
사업시행방식0.5280.4760.4180.4450.5850.4831.0000.4270.0000.503
생활권유형0.2320.5580.3120.6110.2480.3280.4271.0000.0000.632
지목0.5760.1140.1240.0000.1880.3560.0000.0001.0000.291
기본계획명0.5570.5890.2280.7520.4890.4070.5030.6320.2911.000
2024-01-06T09:23:01.945136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번번지시군구명지목지구유형사업시행방식시행자 구분시행단계정비유형생활권유형건폐율기본계획명
순번1.000-0.354-0.1760.7540.0310.2660.4120.4600.3200.5550.5800.3510.651
번-0.3541.000-0.0620.5120.0000.4430.5800.2550.1930.0000.0000.4170.000
지-0.176-0.0621.0000.1800.4650.0000.1190.3160.0000.0000.0000.2650.724
시군구명0.7540.5120.1801.0000.1140.5720.4760.4200.3610.6480.5580.3630.589
지목0.0310.0000.4650.1141.0000.0000.0000.1880.1240.3560.0000.5760.291
지구유형0.2660.4430.0000.5720.0001.0000.4450.1690.2910.0000.6110.3570.752
사업시행방식0.4120.5800.1190.4760.0000.4451.0000.5850.4180.4830.4270.5280.503
시행자 구분0.4600.2550.3160.4200.1880.1690.5851.0000.5750.0940.2480.5110.489
시행단계0.3200.1930.0000.3610.1240.2910.4180.5751.0000.2260.3120.5480.228
정비유형0.5550.0000.0000.6480.3560.0000.4830.0940.2261.0000.3280.3030.407
생활권유형0.5800.0000.0000.5580.0000.6110.4270.2480.3120.3281.0000.2320.632
건폐율0.3510.4170.2650.3630.5760.3570.5280.5110.5480.3030.2321.0000.557
기본계획명0.6510.0000.7240.5890.2910.7520.5030.4890.2280.4070.6320.5571.000

Missing values

2024-01-06T09:22:40.299702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-06T09:22:40.989736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-01-06T09:22:41.276247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

순번정비 구역명시군구명법정동명번지지목지구유형사업시행방식시행자 구분시행단계정비유형생활권유형정비구역 면적(제곱미터)용적률건폐율기본계획명고시번호고시일
01미아제11구역 주택재개발정비사업강북구미아동791108대정비(예정)구역주택정비형재개발조합(단독)추진위구성전면C(대생활권)35,89119060도시주거환경정비제2010-490호2010-12-30
12미아9-2주택재건축정비구역강북구미아동13772대정비(예정)구역재건축(단독)조합(단독)조합설립인가<NA><NA>102,84723120<NA>제2009-301호2009-07-30
23미아4-1구역주택재건축정비사업강북구미아동8373대정비(예정)구역재건축(단독)조합(단독)조합설립인가<NA><NA>51,26519721<NA>제2009-307호2009-08-06
34미아2재정비촉진구역 주택재개발강북구미아동4030대정비(예정)구역(촉)재개발조합(단독)조합설립인가수복(전면)<NA>179,56623016도시주거환경정비제2020-423호2020-10-22
45미아3재정비촉진구역 주택재개발정비사업강북구미아동4390대정비(예정)구역(촉)재개발조합(단독)조합설립인가수복(전면)<NA>57,55322925<NA>제2020-423호2020-10-22
56강북5구역 도시환경정비사업강북구미아동6179대정비(예정)구역(촉)도시환경정비조합(단독)추진위구성<NA><NA>12858953미아균형발전촉진지구 개발제2014-80호2014-03-06
67강북7강북구미아동605대정비구역이 아닌 구역(촉)도시환경정비토지등소유자정비구역지정<NA><NA>11,526<NA><NA><NA>제2014-81호2014-03-06
78방화3재정비촉진구역강서구방화동615103대정비(예정)구역재건축(단독)조합(단독)조합설립인가<NA><NA>90,38322325<NA><NA><NA>
89신안빌라강서구마곡동32753대정비(예정)구역재건축(공동)조합(단독)조합설립인가<NA><NA>16,40026128신안빌라주택재건축정비사업서울특별시고시 2012-2152012-08-09
910봉천1-1구역주택재건축정비사업관악구봉천동72857대정비(예정)구역재건축조합(단독)조합설립인가<NA><NA>34,14227923도시주거환경정비서울특별시고시 제2000475호2009-11-26
순번정비 구역명시군구명법정동명번지지목지구유형사업시행방식시행자 구분시행단계정비유형생활권유형정비구역 면적(제곱미터)용적률건폐율기본계획명고시번호고시일
190191세운2종로구장사동670대재정비촉진지구(촉)도시환경정비토지등소유자정비구역지정전면<NA>38,963600이하60이하<NA>제2014-119호2014-03-27
191192옥인동 47 일대 관리형 주거환경개선사업종로구옥인동470대정비(예정)구역주택정비형재개발시장군수정비구역지정<NA><NA>30,276<NA>60이하<NA>제2021-229호2021-05-13
192193경복궁서측 주거환경개선사업종로구필운동1427도로정비(예정)구역주택정비형재개발시장군수정비구역지정<NA><NA>582,297<NA><NA><NA>제2016-201호2016-07-14
193194부암동 성곽마을 주거환경관리사업종로구부암동26521대정비(예정)구역주거환경개선시장군수정비구역지정<NA><NA>98,118<NA><NA>종로구 부암동 성곽마을(창의문 백악,인왕마을) 관리형 주거환경개선사업 정비구역제2017-269호2017-07-20
194195행촌권 성곽마을 주거환경관리사업종로구행촌동210678대정비(예정)구역주거환경개선시장군수정비구역지정<NA><NA>141,323<NA><NA><NA>제2017-289호2017-08-03
195196신영동 너와나우리마을 관리형 주거환경개선사업종로구신영동2140대정비(예정)구역주거환경개선토지등소유자정비구역지정전면A(소생활권)44,071200이하60이하신영동 너와나우리마을 주거환경관리사업제2018-36호2018-02-22
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